meta.taxa {metamicrobiomeR}

Meta-analysis of taxa/pathway abundance comparison.

Description

This function does meta-analysis based on estimates and standard errors from taxa/pathway abundance comparison using random effect and fixed effect meta-analysis models.

Usage

meta.taxa(
  taxcomdat,
  estimate.pattern = "Estimate.",
  se.pattern = "Std. Error.",
  summary.measure = "RR",
  pool.var = "id",
  studylab = "study",
  backtransform = FALSE,
  percent.meta = 0.5,
  p.adjust.method = "fdr"
)

Arguments

taxcomdat

matrice of estimates and SE of all taxa/pathways combined from all included studies.

estimate.pattern

string pattern for estimates. Default is "Estimate.".

se.pattern

string pattern for standard error. Default is "Std. Error.".

summary.measure

"RR" for estimates from GAMLSS with BEZI family and "RD" for estimates from Linear/linear mixed effect model. Default is "RR"

pool.var

name of id variable for meta-analysis. Default is "id".

studylab

name of variable characterizing included studies. Default is "study".

backtransform

whether or not to perform backtransformation of the estimates. Default is FALSE.

percent.meta

the threshold percentage of number of studies that a taxa is available to do meta-analysis. Default is 0.5

p.adjust.method

method for multiple testing adjustment (available methods of the function p.adjust). Default is "fdr".

Value

a list of matrices of results for all variables in the comparison models.

Examples

# load saved GAMLSS-BEZI results of four studies
# for the comparison of bacterial taxa relative abundance between
# genders adjusted for breastfeeding and infant age at sample collection
data(tabsex4)
#select only taxonomies of a small phylum for meta-analysis example
# (to save running time)
tlm<-tabsex4$id[grep("k__bacteria.p__fusobacteria",tabsex4$id)]
# meta-analysis
metab.sex<-meta.taxa(taxcomdat=tabsex4[tabsex4$id %in% tlm,],
summary.measure="RR", pool.var="id", studylab="study",
backtransform=FALSE, percent.meta=0.5, p.adjust.method="fdr")
#show results by table and plot
#phylum
#table
metatab.show(metatab=metab.sex$random,com.pooled.tab=tabsex4[tabsex4$id %in% tlm,],
tax.lev="l2",showvar="genderMale",p.cutoff.type="p", p.cutoff=1,display="table")